In the past decade, tremendous progress has been made in imaging technologies. Not only have the resolution and sensitivity of imaging sensors improved greatly, but also the hardware costs have decreased. There has also been a corresponding exponential increase in deployment of a variety of imaging sensors for surveillance and situational awareness. However, various conditions during the imaging process, such as weather conditions and imaging system artifacts, still present challenges for automatic image data processing.
Smudges on a lens may be recorded in a video taken by an imaging system. For example, when an imaging system is exposed to weather (e.g. an imaging system on the outside of a building, or on a vehicle) water droplets, or particulate matter such as dust, dirt, or smoke can easily get splashed or blown onto the lens, resulting in smudges on the recorded video. Many other factors, such as fingerprints, contribute to smudge appearances in videos as well. Smudges may be of varying shapes, sizes and appearances.
Such defects complicate the video and reduce the performance of existing intelligent video processing capabilities. In order to prepare existing video for processing by automatic video processing algorithms, current practice requires a person to edit a pre-recorded video manually. This is a time-consuming and labor-intensive procedure. The person has to use editing software to manually remove smudges and, in some cases, reticle lines, if the lens includes these. Despite this intensive effort, manual editing still only provides marginal improvement in video quality, with many inconsistencies still remaining within the video scene.